a = np.array([[0, 2, 0, 0], [0, 1, 3, 0], [0, 0, 10, 11], [0, 0, 1, 7]])
array([[ 0, 2, 0, 0],
[ 0, 1, 3, 0],
[ 0, 0, 10, 11],
[ 0, 0, 1, 7]])
There are 0 entries in each row. I need to assign a value to each of these zero entries, where the value is calculated as follows:
V = 0.1 * Si / Ni
where Si is the sum of row i
Ni is the number of zero entries in row i
I can calculate Si and Ni fairly easy:
S = np.sum(a, axis=1)
array([ 2, 4, 21, 8])
N = np.count_nonzero(a == 0, axis=1)
array([3, 2, 2, 2])
Now, V is calculated as:
V = 0.1 * S/N
array([0.06666667, 0.2 , 1.05 , 0.4 ])
But how do I assign V[i] to a zero entry in i-th row? So I'm expecting to get the following array a:
array([[ 0.06666667, 2, 0.06666667, 0.06666667],
[ 0.2, 1, 3, 0.2],
[ 1.05, 1.05, 10, 11],
[ 0.4, 0.4, 1, 7]])
I need some kind of selective broadcasting operation or assignment?
Use np.where
np.where(a == 0, v.reshape(-1, 1), a)
array([[ 0.06666667, 2. , 0.06666667, 0.06666667],
[ 0.2 , 1. , 3. , 0.2 ],
[ 1.05 , 1.05 , 10. , 11. ],
[ 0.4 , 0.4 , 1. , 7. ]])